Scrap Material Extraction

Accurately identifying, segmenting, and valuing materials is crucial for profitable operations in the scrap material industry. Traditional methods involving manual inspections are labor-intensive, slow, and often inaccurate. By leveraging AI and real-time data analysis using cameras, scrap buyers can enhance the efficiency and accuracy of material identification, leading to better decision-making and resource allocation, reduced operational costs, and quicker response times.

Meet our Client

Our client is a leading scrap material buyer based in Australia. Their expertise lies in identifying high-value materials in large garbage dumps, particularly construction waste. They specialize in purchasing valuable construction scrap materials from dump yards and selling them to companies that use these materials as raw inputs. These materials include bricks, tiles, timber, concrete, and metals such as iron and steel. The client is now focusing on using advanced technology to streamline their operations and improve profitability.

Their Challenge

The client previously relied on mobilizing manpower to each construction dump yard across Australia to manually inspect using heavy machinery and estimate the value of scrap materials that the client could salvage. This process was time-consuming and costly and often resulted in inaccurate valuations. After purchasing the waste materials, clients often segregated the waste in their own warehouses, which was expensive due to logistics. The client needed a more efficient and accurate method to assess the value of scrap materials without the need for extensive manual labor and machinery.

Client Consultation

Initial discussions revealed that the client aimed to leverage real-time object detection analysis to automate the process of material identification and valuation. Their objective was to install cameras in garbage dumps to detect and quantify materials such as iron, concrete, bricks, tiles, and timber and then estimate their fair value. Based on this value, they can correctly quote the purchasers of these raw materials and gain a competitive advantage. Also, the erstwhile manual process of inspection of each and every construction waste dump was expensive and time-consuming. Hence the client wanted to ensure that only profitable scrap piles would warrant further inspection and purchase.

Proposed Solution

We proposed enhancing the client’s material identification process through high-quality data annotation of camera-captured imagery. Our solution involved:

Annotating images to accurately identify and segment materials such as iron, concrete, bricks, tiles, and timber
Providing detailed polygonal annotations to precisely quantify the percentage of each material in the garbage piles.
Enabling real-time analysis and value estimation to facilitate quick decision-making.

Implementation

The core steps in the implementation included:

Analyzing and Processing Images: Reviewing and processing thousands of images captured by cameras installed in garbage dumps.
Image Annotation: Utilizing advanced AI annotation techniques to meticulously label and segment materials into categories of iron, concrete, bricks, tiles and timber, and also to quantify the different materials present.
Real-Time Analysis: Integrating the annotated data into the client’s system for real-time analysis and value estimation.

Results and Impact

The implementation of our data annotation services brought about significant improvements:

Increased Detection Efficiency: The time required to detect and value materials decreased by 75%, enabling quicker response times.
Cost Savings: Reduced manpower requirements led to substantial cost savings, as labor was no longer required to be sent to every dump yard. This significantly cut down operational costs by 60%, amounting to savings of approximately Euro 1.4M annually.
Better Buying & Selling Rates: With empirical data on material value, the client could purchase the scrap at better rates as well as quote better selling rates to buyers, leading to increased profitability and undercutting competition
Reduced Acquisition to Sales Time: Quicker decision-making on purchase and real-time data transmission to prospective customers reduced the time from acquisition to sales.
Lower Storage and Transportation Costs: Efficient operations minimized the need for extensive storage and reduced transportation costs, as buyer interest was determined in real time in the image analysis stage.

Conclusion

The transition to using expertly annotated camera imagery for material identification and valuation represents a significant advancement in the scrap material industry. It not only streamlined the identification process but also highlighted the potential of professional annotation services in optimizing operations. This successful implementation serves as a benchmark for similar companies looking to leverage technology for improved efficiency and profitability.